[][src]Crate sorer


SoRer, short for schema-on-read-er, is a program that can read files in the SoR format and build columnar dataframes based on a dynamically inferred schema.

SoRer was built with speed and memory efficiency in mind so it can handle processing files that are too large to fit into RAM.

On our 2 year old desktop computer with a SATA SSD (meaning our testing is likely near being bottlenecked by ssd read speeds) and 4 cores (4 threads), SoRer can parse at ~400 MB/s on a large test file with 8 columns, two of each data type with random values (which can be generated by running cargo run --release --bin generate (warning don't do this inside of Docker, you must install rust if you want to do this due to file i/o overhead when using Docker). In a best case scenario, on a large file with 3 columns of random bools, it can parse at over 700 MB/s


Building SoRer

SoRer can be built on any computer by running the command: make docker from the root of this repository. This builds a Docker image tagged as sorer. It also builds the executable for sorer, located at /sorer/target/release/sorer and copies over the executable to the current directory.

Tests can be ran by running the command make test. The program can be ran against a small test file named sor.txt by running the command: make run.

Documentation can be built by running the command make doc. This builds the documentation and copies it to ./doc/ on the host filesystem in this directory. This documentation can be viewed by opening ./doc/sorer/index.html in your broswer.

Note that ideally the best way to run our program is bare metal due to overhead for using Docker (especially on Windows or Mac). You can do that by installing rust by running the following command:

curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh

Follow the printed instructions to source the cargo environment variables after installing.

Then build sorer by running cargo build --release. You may test the program after installing rust by running cargo test. Documentation may be built by running cargo doc --no-deps --open.

Running SoRer

SoRer is ran as a command line tool that prints its results to stdout.

The command line arguments are summarized in the below table

argumentvalue typerequired?description
-f<string>yespath to SoR file
-from<uint>nostarting position in file (in bytes)
-len<uint>nonumber of bytes to read
-print_col_type<uint>dependsprint the type of a column: BOOL, INT, FLOAT, STRING
-print_col_idx<uint> <uint>dependsthe first argument is the column, the second is the offset
-is_missing_idx<uint> <uint>dependsis there a missing field in the specified column offset

When <val> in -from <val> is greater than 0, then the file is read starting from the first complete line after <val>.

When <val> in -len <val> is greater than 0, then the file is read up until the last complete line.

After running make build, running make bash will mount the current the current directory to the docker container and start bash. If you want to test any large files, you should do make build first, then copy the files into this directory, then run make bash. Once you're in bash, you can interact with sorer as usual:

SoR Files

A SoR file is stored as plain text. Files consists of a sequence of rows, each row must be separated by the newline character, "\n". Each row is a sequence of fields, each field starting with "<" and ending with ">". Spaces around delimiters are ignored.

SoR Fields

A field can be either missing a value, or contain a value of one of four SoR types:

  • String
  • Float
  • Integer
  • Bool
TypeAllowed values
StringEither as a sequences of characters without spaces or as a double quote delimited sequence of characters with spaces. Line breaks are not allowed in Strings. Can't be longer than 255 characters. Must be valid utf-8 characters.
FloatAny C++ float
IntegerAny C++ integer, ie a sequence of digits with an optional leading sign (must not be separated by whitespace)
bool{1, 0}
Missing (aka Null)must be empty, ie "<>"

Valid Examples of SoR Fields

The following is an example of a row with four fields:

< 1 > < hi >< +2.2 > < " bye ">

The following is an example of a row with explicit missing fields:

<1> <bye> <> <>

The following is also valid:

<> <> <> <>

Invalid Examples of SoR Fields

<1. 2>       // space after dot

<bye world>  // string with spaces and without quotes

<+ 1>        // space after the +

NOTE: If a SoR file contains an invalid field, the row will be discarded for both schema inference and data parsing.

Schema Inference

The schema that SoRer generates depends on the data types contained in the row with the most number of fields in the first 500 rows (or the whole file, whichever comes first), irregardless of the -from command line argument. The data type chosen for each column in the schema depends on the precedence of the data type. Based on this data type precedence, a schema is inferred and then applied to all fields in that column.

The Data Type precedence is as follows:

  1. String
  2. Float
  3. Integer
  4. Bool

This means that if any value is a String, the whole column is parsed into a String type. Otherwise, if any of the values is a Float, then the column is of Float type. Otherwise, if you find a value with a sign or a value larger than 1, then the column is Integer. Otherwise the column is a Bool type.

Rows that don't match the schema

If a row that doesn't match the schema is found after the schema is inferred (meaning after the first 500 lines), then the row is discarded. An example is if a schema is parsed as <int> <int>, but a line coming after the first 500 has <string> <int>, then it will be discarded.

Note however, that it is valid for two rows in the same file to have a different number of fields and still be considered to match the schema. For rows with more fields than the schema, the extra fields will be discarded but the row will still be parsed as long as the other fields match the schema.

E.g. The schema: <int> <bool> and a row: <12> <0> <discarded> parses to <12><0>

If a row has less fields without explicit missing fields (i.e. "<>"), aka implicit missing fields, SoRer will attempt to parse the fields according to the schema and fill in explicit missing fields at the end of the row until it matches the number of fields in the schema.

E.g. The schema: <int> <bool> <string> and a row: <12> parses to <12><><>



This module defines helper methods to interact with a DataFrame A DataFrame is a columnar representation of a SOR file and is represented as a Vec<Column>


A module for parsing raw byte slices into SoR data.


A module for inferring SoR schemas.